BC tree-based spectral sampling for big complex network visualization

Graph sampling methods have been used to reduce the size and complexity of big complex networks for graph mining and visualization. However, existing graph sampling methods often fail to preserve the connectivity and important structures of the original graph. This paper introduces a new divide and...

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Vydáno v:Applied network science Ročník 6; číslo 1; s. 1 - 23
Hlavní autoři: Hu, Jingming, Chu, Tuan Tran, Hong, Seok-Hee, Chen, Jialu, Meidiana, Amyra, Torkel, Marnijati, Eades, Peter, Ma, Kwan-Liu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 21.08.2021
Springer Nature B.V
SpringerOpen
Témata:
Algorithms
Apexes
Complexity
Computer Appl. in Social and Behavioral Sciences
Computer networks
Computer Science
Connectivity
Decomposition
Distributed processing
Graph sampling
Graph theory
Run time (computers)
Sampling methods
Simulation and Modeling
Special Issue of the 9th International Conference on Complex Networks and Their Applications
Spectra
Spectral sparsification
Visualization
Connectivity
Graph sampling
Spectral sparsification
ISSN:2364-8228, 2364-8228
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Shrnutí:Graph sampling methods have been used to reduce the size and complexity of big complex networks for graph mining and visualization. However, existing graph sampling methods often fail to preserve the connectivity and important structures of the original graph. This paper introduces a new divide and conquer approach to spectral graph sampling based on graph connectivity, called the BC Tree (i.e., decomposition of a connected graph into biconnected components) and spectral sparsification. Specifically, we present two methods, spectral vertex sampling B C _ S V and spectral edge sampling B C _ S S by computing effective resistance values of vertices and edges for each connected component. Furthermore, we present D B C _ S S and D B C _ G D , graph connectivity-based distributed algorithms for spectral sparsification and graph drawing respectively, aiming to further improve the runtime efficiency of spectral sparsification and graph drawing by integrating connectivity-based graph decomposition and distributed computing. Experimental results demonstrate that B C _ S V and B C _ S S are significantly faster than previous spectral graph sampling methods while preserving the same sampling quality. D B C _ S S and D B C _ G D obtain further significant runtime improvement over sequential approaches, and D B C _ G D further achieves significant improvements in quality metrics over sequential graph drawing layouts.
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ISSN:2364-8228
2364-8228
DOI:10.1007/s41109-021-00405-3